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Advanced Data Analysis (ECOM90025)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

Availability
Semester 2
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This subject covers a range of statistical methods and computational approaches that are applicable in business and economics. This will include a review of data types and regression, and statistical methods for prediction, classification, and causal analysis.  

Intended learning outcomes

On completion of this subject, students should be able to:

  • Implement computational methods to prepare, explore and describe a range of types of data.
  • Apply regression and extensions including trees, random forests and regularised estimation for computing and evaluating predictions.
  • Apply and critique nonparametric regression methods for estimation and prediction.
  • Apply and compare methods for statistical classification.
  • Implement, compare, and critique simple econometric frameworks for causal analysis including experiments, matching and difference-in-differences.

Generic skills

  • High level of development: written communication; application of theory to practice; interpretation and analysis; critical thinking; synthesis of data and other information; evaluation of data and other information; statistical reasoning; use of computer software.
  • Moderate level of development: problem solving; accessing data and other information from a range of sources.
  • Some level of development: oral communication; collaborative learning; receptiveness to alternative ideas.

Last updated: 8 November 2024